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+# Quantum Notebooks
+
+This directory contains Jupyter notebooks designed for exploring and demonstrating various quantum computing concepts and algorithms. Each notebook provides hands-on examples and visualizations to facilitate understanding of quantum algorithms and their applications.
+
+## Table of Contents
+
+- [Quantum Machine Learning Demo](#quantum-machine-learning-demo)
+- [Hybrid Quantum-Classical Algorithms Demo](#hybrid-quantum-classical-algorithms-demo)
+- [Quantum Annealing Analysis](#quantum-annealing-analysis)
+- [Grover's Algorithm Analysis](#grovers-algorithm-analysis)
+- [Quantum Key Distribution Demo](#quantum-key-distribution-demo)
+- [Variational Quantum Eigensolver Demo](#variational-quantum-eigensolver-demo)
+
+## Notebooks
+
+### Quantum Machine Learning Demo
+- **File**: `quantum_machine_learning_demo.ipynb`
+- **Description**: This notebook demonstrates the application of quantum machine learning techniques using the Variational Quantum Classifier (VQC). It includes data preparation, training the VQC, and evaluating its performance on a binary classification problem.
+
+### Hybrid Quantum-Classical Algorithms Demo
+- **File**: `hybrid_quantum_classical_demo.ipynb`
+- **Description**: This notebook demonstrates the implementation of hybrid quantum-classical algorithms using the Quantum Approximate Optimization Algorithm (QAOA). It includes problem definition, circuit construction, and optimization to find the maximum of a given objective function.
+
+### Quantum Annealing Analysis
+- **File**: `quantum_annealing_analysis.ipynb`
+- **Description**: This notebook provides an analysis of quantum annealing, demonstrating its application to optimization problems. It includes problem setup, implementation of quantum annealing, and performance analysis.
+
+### Grover's Algorithm Analysis
+- **File**: `grover_analysis.ipynb`
+- **Description**: This notebook analyzes Grover's algorithm, which is used for searching an unsorted database. It includes implementation, success probability analysis, and performance evaluation.
+
+### Quantum Key Distribution Demo
+- **File**: `qkd_demo.ipynb`
+- **Description**: This notebook demonstrates Quantum Key Distribution (QKD) using the BB84 protocol. It includes key generation, measurement processes, and security analysis.
+
+### Variational Quantum Eigensolver Demo
+- **File**: `vqe_demo.ipynb`
+- **Description**: This notebook demonstrates the Variational Quantum Eigensolver (VQE) algorithm to find the ground state energy of a quantum system. It includes Hamiltonian definition, ansatz circuit construction, and VQE execution.
+
+## Requirements
+
+To run the notebooks, ensure you have the following packages installed:
+
+- Qiskit
+- NumPy
+- Matplotlib
+- scikit-learn
+
+You can install the required packages using pip:
+
+```bash
+pip install qiskit numpy matplotlib scikit-learn
+```
+
+## Usage
+To use the notebooks, open them in Jupyter Notebook or JupyterLab. You can run the cells interactively to explore the concepts and algorithms presented in each notebook.
+
+## Contributing
+Contributions to the notebooks are welcome! If you have suggestions for improvements or new demonstrations to implement, please open an issue or submit a pull request.
+
+## License
+This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.